Although they obtain good performances with this pseudo-labeling technique, the Student model is still influenced by the Teacher's erroneous predictions.
The quality of these predictions is difficult to control by applying only a simple confidence thresholding.
For this reason, there is a need for an additional way to strengthen the region proposals and reduce the number of erroneous predictions by the Teacher.
In the proposed architecture, we introduce a new task used for the classification of the bboxes, with the aim of distinguishing good quality ones from the others.
This new score exploits the information complementary to the class score already used in these networks, allowing a different level of filtering. In addition, we show how to take advantage of the regression tasks on the unsupervised learning part.
Usually, they are excluded in the unsupervised training phase.
The justification is that the classification score is not able to filter the potentially incorrect bboxes [1]}, [2]}.
A well-known problem during training is the Objective Imbalance [3]}, which is characterized by the difficulty in balancing the different loss contributions.
In our hypothesis, this is the case for the unsupervised training part.
In order to obtain a positive effect from regression losses, an adequate balance of the contribution of these two losses (regression and category classification) is a possible solution to the above-mentioned problem.
In this way, we prevent the regression losses on unsupervised dataset from dominating the training, a phenomenon that greatly amplifies the error introduced by inaccurate Teacher predictions.
